Blind Light Field Image Quality Assessment via Frequency Domain Analysis and Auxiliary Learning

Abstract

Due to the distortions occurring at various stages from acquisition to visualization, light field image quality assessment (LFIQA) is crucial for guiding the processing of light field images (LFIs). In this letter, we propose a new blind LFIQA metric via frequency domain analysis and auxiliary learning, termed as FABLFQA. First, spatial-angular patches are extracted from LFIs and further processed through discrete cosine transform to obtain light field frequency maps. Subsequently, a concise and efficient frequency-aware deep learning network is designed to extract frequency features, including the frequency descriptor, 3D ConvBlock, and frequency transformer. Finally, a distortion type discrimination auxiliary task is employed to facilitate the learning of the main quality assessment task. Experimental results on three representative LFI datasets show that the proposed metric outperforms the state-of-the-art metrics.